/////////////////////////////////////////// // Running convGAN on folding_car_good /////////////////////////////////////////// Load 'data_input/folding_car_good' from pickle file Data loaded. -> Shuffling data ### Start exercise for synthetic point generator ====== Step 1/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 1/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 177, 155 LR fn, tp: 6, 8 LR f1 score: 0.090 LR cohens kappa score: 0.017 LR average precision score: 0.059 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 7, 7 GB f1 score: 0.583 GB cohens kappa score: 0.569 -> test with 'KNN' KNN tn, fp: 305, 27 KNN fn, tp: 0, 14 KNN f1 score: 0.509 KNN cohens kappa score: 0.478 ------ Step 1/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 175, 157 LR fn, tp: 3, 11 LR f1 score: 0.121 LR cohens kappa score: 0.050 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 6, 8 GB f1 score: 0.696 GB cohens kappa score: 0.686 -> test with 'KNN' KNN tn, fp: 306, 26 KNN fn, tp: 0, 14 KNN f1 score: 0.519 KNN cohens kappa score: 0.488 ------ Step 1/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 172, 160 LR fn, tp: 6, 8 LR f1 score: 0.088 LR cohens kappa score: 0.014 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 6, 8 GB f1 score: 0.696 GB cohens kappa score: 0.686 -> test with 'KNN' KNN tn, fp: 321, 11 KNN fn, tp: 1, 13 KNN f1 score: 0.684 KNN cohens kappa score: 0.667 ------ Step 1/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 176, 156 LR fn, tp: 3, 11 LR f1 score: 0.122 LR cohens kappa score: 0.051 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 315, 17 KNN fn, tp: 2, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.533 ------ Step 1/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 179, 152 LR fn, tp: 3, 10 LR f1 score: 0.114 LR cohens kappa score: 0.048 LR average precision score: 0.057 -> test with 'GB' GB tn, fp: 330, 1 GB fn, tp: 2, 11 GB f1 score: 0.880 GB cohens kappa score: 0.875 -> test with 'KNN' KNN tn, fp: 320, 11 KNN fn, tp: 1, 12 KNN f1 score: 0.667 KNN cohens kappa score: 0.650 ====== Step 2/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 2/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 165, 167 LR fn, tp: 4, 10 LR f1 score: 0.105 LR cohens kappa score: 0.032 LR average precision score: 0.067 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 1, 13 KNN f1 score: 0.813 KNN cohens kappa score: 0.804 ------ Step 2/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 169, 163 LR fn, tp: 3, 11 LR f1 score: 0.117 LR cohens kappa score: 0.046 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 1, 13 GB f1 score: 0.897 GB cohens kappa score: 0.892 -> test with 'KNN' KNN tn, fp: 315, 17 KNN fn, tp: 2, 12 KNN f1 score: 0.558 KNN cohens kappa score: 0.533 ------ Step 2/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 3, 11 LR f1 score: 0.131 LR cohens kappa score: 0.061 LR average precision score: 0.070 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 7, 7 GB f1 score: 0.667 GB cohens kappa score: 0.657 -> test with 'KNN' KNN tn, fp: 316, 16 KNN fn, tp: 1, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.582 ------ Step 2/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 7, 7 LR f1 score: 0.085 LR cohens kappa score: 0.012 LR average precision score: 0.050 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 4, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 296, 36 KNN fn, tp: 2, 12 KNN f1 score: 0.387 KNN cohens kappa score: 0.346 ------ Step 2/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 189, 142 LR fn, tp: 5, 8 LR f1 score: 0.098 LR cohens kappa score: 0.031 LR average precision score: 0.073 -> test with 'GB' GB tn, fp: 328, 3 GB fn, tp: 3, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 304, 27 KNN fn, tp: 0, 13 KNN f1 score: 0.491 KNN cohens kappa score: 0.460 ====== Step 3/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 3/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 170, 162 LR fn, tp: 3, 11 LR f1 score: 0.118 LR cohens kappa score: 0.046 LR average precision score: 0.077 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 4, 10 GB f1 score: 0.800 GB cohens kappa score: 0.793 -> test with 'KNN' KNN tn, fp: 316, 16 KNN fn, tp: 1, 13 KNN f1 score: 0.605 KNN cohens kappa score: 0.582 ------ Step 3/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 189, 143 LR fn, tp: 4, 10 LR f1 score: 0.120 LR cohens kappa score: 0.049 LR average precision score: 0.066 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 0, 14 GB f1 score: 0.933 GB cohens kappa score: 0.930 -> test with 'KNN' KNN tn, fp: 319, 13 KNN fn, tp: 1, 13 KNN f1 score: 0.650 KNN cohens kappa score: 0.631 ------ Step 3/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 182, 150 LR fn, tp: 5, 9 LR f1 score: 0.104 LR cohens kappa score: 0.032 LR average precision score: 0.056 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 8, 6 GB f1 score: 0.522 GB cohens kappa score: 0.506 -> test with 'KNN' KNN tn, fp: 314, 18 KNN fn, tp: 1, 13 KNN f1 score: 0.578 KNN cohens kappa score: 0.553 ------ Step 3/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 180, 152 LR fn, tp: 3, 11 LR f1 score: 0.124 LR cohens kappa score: 0.054 LR average precision score: 0.075 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 4, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 302, 30 KNN fn, tp: 0, 14 KNN f1 score: 0.483 KNN cohens kappa score: 0.449 ------ Step 3/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 168, 163 LR fn, tp: 5, 8 LR f1 score: 0.087 LR cohens kappa score: 0.018 LR average precision score: 0.051 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 3, 10 GB f1 score: 0.800 GB cohens kappa score: 0.792 -> test with 'KNN' KNN tn, fp: 298, 33 KNN fn, tp: 0, 13 KNN f1 score: 0.441 KNN cohens kappa score: 0.406 ====== Step 4/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 4/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 169, 163 LR fn, tp: 3, 11 LR f1 score: 0.117 LR cohens kappa score: 0.046 LR average precision score: 0.063 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 4, 10 GB f1 score: 0.769 GB cohens kappa score: 0.760 -> test with 'KNN' KNN tn, fp: 327, 5 KNN fn, tp: 0, 14 KNN f1 score: 0.848 KNN cohens kappa score: 0.841 ------ Step 4/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 178, 154 LR fn, tp: 6, 8 LR f1 score: 0.091 LR cohens kappa score: 0.018 LR average precision score: 0.063 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 311, 21 KNN fn, tp: 4, 10 KNN f1 score: 0.444 KNN cohens kappa score: 0.412 ------ Step 4/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 172, 160 LR fn, tp: 4, 10 LR f1 score: 0.109 LR cohens kappa score: 0.037 LR average precision score: 0.066 -> test with 'GB' GB tn, fp: 332, 0 GB fn, tp: 5, 9 GB f1 score: 0.783 GB cohens kappa score: 0.775 -> test with 'KNN' KNN tn, fp: 303, 29 KNN fn, tp: 1, 13 KNN f1 score: 0.464 KNN cohens kappa score: 0.430 ------ Step 4/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 191, 141 LR fn, tp: 6, 8 LR f1 score: 0.098 LR cohens kappa score: 0.026 LR average precision score: 0.054 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 6, 8 GB f1 score: 0.667 GB cohens kappa score: 0.655 -> test with 'KNN' KNN tn, fp: 302, 30 KNN fn, tp: 0, 14 KNN f1 score: 0.483 KNN cohens kappa score: 0.449 ------ Step 4/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 185, 146 LR fn, tp: 1, 12 LR f1 score: 0.140 LR cohens kappa score: 0.076 LR average precision score: 0.079 -> test with 'GB' GB tn, fp: 329, 2 GB fn, tp: 7, 6 GB f1 score: 0.571 GB cohens kappa score: 0.559 -> test with 'KNN' KNN tn, fp: 317, 14 KNN fn, tp: 1, 12 KNN f1 score: 0.615 KNN cohens kappa score: 0.595 ====== Step 5/5 ======= -> Shuffling data -> Spliting data to slices ------ Step 5/5: Slice 1/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 190, 142 LR fn, tp: 8, 6 LR f1 score: 0.074 LR cohens kappa score: 0.000 LR average precision score: 0.051 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 7, 7 GB f1 score: 0.636 GB cohens kappa score: 0.625 -> test with 'KNN' KNN tn, fp: 314, 18 KNN fn, tp: 3, 11 KNN f1 score: 0.512 KNN cohens kappa score: 0.483 ------ Step 5/5: Slice 2/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 192, 140 LR fn, tp: 4, 10 LR f1 score: 0.122 LR cohens kappa score: 0.052 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 330, 2 GB fn, tp: 7, 7 GB f1 score: 0.609 GB cohens kappa score: 0.596 -> test with 'KNN' KNN tn, fp: 324, 8 KNN fn, tp: 2, 12 KNN f1 score: 0.706 KNN cohens kappa score: 0.691 ------ Step 5/5: Slice 3/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 169, 163 LR fn, tp: 2, 12 LR f1 score: 0.127 LR cohens kappa score: 0.056 LR average precision score: 0.069 -> test with 'GB' GB tn, fp: 329, 3 GB fn, tp: 5, 9 GB f1 score: 0.692 GB cohens kappa score: 0.680 -> test with 'KNN' KNN tn, fp: 311, 21 KNN fn, tp: 1, 13 KNN f1 score: 0.542 KNN cohens kappa score: 0.514 ------ Step 5/5: Slice 4/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 162, 170 LR fn, tp: 3, 11 LR f1 score: 0.113 LR cohens kappa score: 0.041 LR average precision score: 0.071 -> test with 'GB' GB tn, fp: 331, 1 GB fn, tp: 8, 6 GB f1 score: 0.571 GB cohens kappa score: 0.560 -> test with 'KNN' KNN tn, fp: 316, 16 KNN fn, tp: 0, 14 KNN f1 score: 0.636 KNN cohens kappa score: 0.615 ------ Step 5/5: Slice 5/5 ------- -> Reset the GAN -> Train generator for synthetic samples -> create 1272 synthetic samples -> test with 'LR' LR tn, fp: 179, 152 LR fn, tp: 4, 9 LR f1 score: 0.103 LR cohens kappa score: 0.036 LR average precision score: 0.061 -> test with 'GB' GB tn, fp: 331, 0 GB fn, tp: 4, 9 GB f1 score: 0.818 GB cohens kappa score: 0.812 -> test with 'KNN' KNN tn, fp: 305, 26 KNN fn, tp: 0, 13 KNN f1 score: 0.500 KNN cohens kappa score: 0.470 ### Exercise is done. -----[ LR ]----- maximum: LR tn, fp: 192, 170 LR fn, tp: 8, 12 LR f1 score: 0.140 LR cohens kappa score: 0.076 LR average precision score: 0.079 average: LR tn, fp: 178.24, 153.56 LR fn, tp: 4.16, 9.64 LR f1 score: 0.109 LR cohens kappa score: 0.038 LR average precision score: 0.065 minimum: LR tn, fp: 162, 140 LR fn, tp: 1, 6 LR f1 score: 0.074 LR cohens kappa score: 0.000 LR average precision score: 0.050 -----[ GB ]----- maximum: GB tn, fp: 332, 3 GB fn, tp: 8, 14 GB f1 score: 0.933 GB cohens kappa score: 0.930 average: GB tn, fp: 330.12, 1.68 GB fn, tp: 5.08, 8.72 GB f1 score: 0.714 GB cohens kappa score: 0.704 minimum: GB tn, fp: 328, 0 GB fn, tp: 0, 6 GB f1 score: 0.522 GB cohens kappa score: 0.506 -----[ KNN ]----- maximum: KNN tn, fp: 327, 36 KNN fn, tp: 4, 14 KNN f1 score: 0.848 KNN cohens kappa score: 0.841 average: KNN tn, fp: 312.16, 19.64 KNN fn, tp: 1.0, 12.8 KNN f1 score: 0.572 KNN cohens kappa score: 0.546 minimum: KNN tn, fp: 296, 5 KNN fn, tp: 0, 10 KNN f1 score: 0.387 KNN cohens kappa score: 0.346